Quantitative Biology

The remarkable accuracy and versatility of single-molecule techniques make possible new measurements that are not feasible in bulk assays. Among these, the precise estimation of folding free energies using fluctuation theorems in nonequilibrium pulling experiments has become a benchmark in modern biophysics. In practice, the use of fluctuation relations to determine free energies requires a thorough evaluation of the usually large energetic contributions caused by the elastic deformation of the different elements of the experimental setup (such as the optical trap, the molecular linkers and the stretched-unfolded polymer). We review and describe how to optimally estimate such elastic energy contributions to extract folding free energies, using DNA and RNA hairpins as model systems pulled by laser optical tweezers. The methodology is generally applicable to other force-spectroscopy techniques and molecular systems.

Ions in channels have been imagined as hard balls in a macroscopic mechanical model, for a very long time. Hard balls interact by collisions in such models, randomly knocking each other on and off `binding' sites in thermal motion. But ions have large charge, and the hard balls of classical models do not. The electrodynamics of charge guarantee strong correlations between the movements of ions on all time scales, even those of atomic scale thermal motion. Correlations are present whenever Maxwell's equations apply, so they are present in individual trajectories, not just averages. Indeed, in a series system like an idealized narrow channel, the correlation is perfect (within the accuracy of Maxwell's equations) because of conservation of total current (that includes Maxwell's displacement current, the ethereal ε 0 ∂E/∂t ).The ethereal component of current prevents spatial variation of total current in a series system. The stochastic complexity of spatial thermal motion disappears for current }in a series system. Total current does not depend on location in a series system like a narrow ion channel on any time scale. Spatial variables are not needed in the description of total current in a one dimensional channel on any time scale, according to the Maxwell equations. Removing the spatial dependence of total current should dramatically simplify theories and simulations, one might imagine.

The purpose of this work is to extend the Geant4-DNA Monte Carlo toolkit to include electron interactions with the four DNA bases using a set of cross sections recently implemented in Geant-DNA CPA100 models and available for liquid water. Electron interaction cross sections for elastic scattering, ionisation, and electronic excitation were calculated in the four DNA bases adenine, thymine, guanine and cytosine. The electron energy range is extended to include relativistic electrons. Elastic scattering cross sections were calculated using the independent atom model with amplitude derived from ELSEPA code. Relativistic Binary Encounter Bethe Vriens model was used to calculate ionisation cross sections. The electronic excitation cross sections calculations were based on the water cross sections following the same strategy used in CPA100 code. These were implemented within the Geant4-DNA option6 physics constructor to extend its capability of tracking electrons in DNA material in addition to liquid water. Since DNA nucleobases have different molecular structure than water it is important to perform more accurate simulations especially because DNA is considered the most radiosensitive structure in cells. Differential and integrated cross sections calculations were in good agreement with data from the literature for all DNA bases. Stopping power, range and inelastic mean free path calculations in the four DNA bases using this new extension of Geant4-DNA option6 are in good agreement with calculations done by other studies, especially for high energy electrons. Some deviations are shown at the low electron energy range, which could be attributed to the different interaction models. Comparison with water simulations shows obvious difference which emphasizes the need to include DNA bases cross sections in track structure codes for better estimation of radiation effects on biological material.

Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited. Whether performance in such tasks can be improved by using large repositories of tangentially related structural data remains an open question. To address this question, we focused on a central problem in biology: predicting how proteins interact with one another---that is, which surfaces of one protein bind to those of another protein. We built a training dataset, the Database of Interacting Protein Structures (DIPS), that contains biases but is two orders of magnitude larger than those used previously. We found that these biases significantly degrade the performance of existing methods on gold-standard data. Hypothesizing that assumptions baked into the hand-crafted features on which these methods depend were the source of the problem, we developed the first end-to-end learning model for protein interface prediction, the Siamese Atomic Surfacelet Network (SASNet). Using only spatial coordinates and identities of atoms, SASNet outperforms state-of-the-art methods trained on gold-standard structural data, even when trained on only 3% of our new dataset. Code and data available at this https URL.

Mikhail Shapiro's lab investigated a promising new method for non-invasive control of calcium currents in individual cells in the nervous system by the selective heating of nanoparticles and show that simple physical laws, properly applied, explain what is happening, and so can be a foundation for constructing improved methods and techniques. They use custom built instrumentation and detailed quantitative measurement to show that special surface properties are not needed to explain their results. Their work is taken as an example of the need for quantitative measurement in biophysics and biology in general. Classical examples are cited and the argument is made that the success of structural and molecular biology has hidden the need for quantitative measurements and controls. Semiconductor and computational electronics is cited as a science even more successful than structural and molecular biology that depends on quantitative measurement, controls, and analysis.

Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative to predict such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent proteins' and encounter complexes' 3D structures as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that pool interacting nodes' features through edge features so that generalized interaction energies can be learned directly from graph data. The resulting energy-based graph convolutional networks (EGCN) with multi-head attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state-of-the-art scoring function for model ranking, EGCN has significantly improved ranking for a CAPRI test set involving homology docking; and is comparable for Score_set, a CAPRI benchmark set generated by diverse community-wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.

DNA surface-hybridization biosensors utilize the selective hybridization of target sequences in solution to surface-immobilized probes. In this process, the target is usually assumed to be in excess, so that its concentration does not significantly vary while hybridizing to the surface-bound probes. If the target is initially at low concentrations and/or if the number of probes is very large and have high affinity for the target, the DNA in solution may get depleted. In this paper we analyze the equilibrium and kinetics of hybridization of DNA biosensors in the case of strong target depletion, by extending the Langmuir adsorption model. We focus, in particular, on the detection of a small amount of a single-nucleotide "mutant" sequence (concentration c 2 ) in a solution, which differs by one or more nucleotides from an abundant "wild-type" sequence (concentration c 1 ≫ c 2 ). We show that depletion can give rise to a strongly-enhanced sensitivity of the biosensors. Using representative values of rate constants and hybridization free energies, we find that in the depletion regime one could detect relative concentrations c 2 / c 1 that are up to three orders of magnitude smaller than in the conventional approach. The kinetics is surprisingly rich, and exhibits a non-monotonic adsorption with no counterpart in the no-depletion case. Finally, we show that, alongside enhanced detection sensitivity, this approach offers the possibility of sample enrichment, by substantially increasing the relative amount of the mutant over the wild-type sequence.

Background: Coronavirus disease 2019 (COVID-19) and Influenza A are common disease caused by viral infection. The clinical symptoms and transmission routes of the two diseases are similar. However, there are no relevant studies on laboratory diagnostic models to discriminate COVID-19 and influenza A. This study aims at establishing a signature of laboratory findings to tell patients with COVID-19 apart from those with influenza A perfectly. Materials: In this study, 56 COVID-19 patients and 54 influenza A patients were included. Laboratory findings, epidemiological characteristics and demographic data were obtained from electronic medical record databases. Elastic network models, followed by a stepwise logistic regression model were implemented to identify indicators capable of discriminating COVID-19 and influenza A. A nomogram is diagramed to show the resulting discriminative model. Results: The majority of hematological and biochemical parameters in COVID-19 patients were significantly different from those in influenza A patients. In the final model, albumin/globulin (A/G), total bilirubin (TBIL) and erythrocyte specific volume (HCT) were selected as predictors. Using an external dataset, the model was validated to perform well. Conclusion: A diagnostic model of laboratory findings was established, in which A/G, TBIL and HCT were included as highly relevant indicators for the segmentation of COVID-19 and influenza A, providing a complimentary means for the precise diagnosis of these two diseases.

Proteins are essential for maintaining life. For example, knowing the structure of a protein, cell regulatory mechanisms of organisms can be modeled, supporting the development of disease treatments or the understanding of relationships between protein structures and food attributes. However, discovering the structure of a protein can be a difficult and expensive task, since it is hard to explore the large search to predict even a small protein. Template-based methods (coarse-grained, homology, threading etc) depend on Prior Knowledge (PK) of proteins determined using other methods as X-Ray Crystallography or Nuclear Magnetic Resonance. On the other hand, template-free methods (full-atom and ab initio) rely on atoms physical-chemical properties to predict protein structures. In comparison with other approaches, the Estimation of Distribution Algorithms (EDAs) can require significant less PK, suggesting that it could be adequate for proteins of low-level of PK. Finding an EDA able to handle both prediction quality and computational time is a difficult task, since they are strong inversely correlated. We developed an EDA specific for the ab initio Protein Structure Prediction (PSP) problem using full-atom representation. We developed one univariate and two bivariate probabilistic models in order to design a proper EDA for PSP. The bivariate models make relationships between dihedral angles ϕ and ψ within an amino acid. Furthermore, we compared the proposed EDA with other approaches from the literature. We noticed that even a relatively simple algorithm such as Random Walk can find the correct solution, but it would require a large amount of prior knowledge (biased prediction). On the other hand, our EDA was able to correctly predict with no prior knowledge at all, characterizing such a prediction as pure ab initio.

We here report on non-equilibrium targeted Molecular Dynamics simulations as tool for the estimation of protein-ligand unbinding kinetics. Correlating simulations with experimental data from SPR kinetics measurements and X-ray crystallography on two small molecule compound libraries bound to the N-terminal domain of the chaperone Hsp90, we show that the mean non-equilibrium work computed in an ensemble of trajectories of enforced ligand unbinding is a promising predictor for ligand unbinding rates. We furthermore investigate the molecular basis determining unbinding rates within the compound libraries. We propose ligand conformational changes and protein-ligand nonbonded interactions to impact on unbinding rates. Ligands may remain longer at the protein if they exhibit strong electrostatic and/or van der Waals interactions with the target. In the case of ligands with rigid chemical scaffold that exhibit longer residence times however, transient electrostatic interactions with the protein appear to facilitate unbinding. Our results imply that understanding the unbinding pathway and the protein-ligand interactions along this path is crucial for the prediction of small molecule ligands with defined unbinding